--- jsr166/src/main/java/util/Random.java 2005/10/02 07:10:59 1.11 +++ jsr166/src/main/java/util/Random.java 2010/09/05 21:32:19 1.26 @@ -1,13 +1,32 @@ /* - * %W% %E% + * Copyright (c) 1995, 2008, Oracle and/or its affiliates. All rights reserved. + * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. * - * Copyright 2005 Sun Microsystems, Inc. All rights reserved. - * SUN PROPRIETARY/CONFIDENTIAL. Use is subject to license terms. + * This code is free software; you can redistribute it and/or modify it + * under the terms of the GNU General Public License version 2 only, as + * published by the Free Software Foundation. Sun designates this + * particular file as subject to the "Classpath" exception as provided + * by Sun in the LICENSE file that accompanied this code. + * + * This code is distributed in the hope that it will be useful, but WITHOUT + * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + * version 2 for more details (a copy is included in the LICENSE file that + * accompanied this code). + * + * You should have received a copy of the GNU General Public License version + * 2 along with this work; if not, write to the Free Software Foundation, + * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. + * + * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA + * or visit www.oracle.com if you need additional information or have any + * questions. */ package java.util; import java.io.*; import java.util.concurrent.atomic.AtomicLong; +import sun.misc.Unsafe; /** * An instance of this class is used to generate a stream of @@ -15,27 +34,36 @@ import java.util.concurrent.atomic.Atomi * modified using a linear congruential formula. (See Donald Knuth, * The Art of Computer Programming, Volume 2, Section 3.2.1.) *
- * If two instances of Random
are created with the same
+ * If two instances of {@code Random} are created with the same
* seed, and the same sequence of method calls is made for each, they
* will generate and return identical sequences of numbers. In order to
* guarantee this property, particular algorithms are specified for the
- * class Random. Java implementations must use all the algorithms
- * shown here for the class Random, for the sake of absolute
- * portability of Java code. However, subclasses of class Random
+ * class {@code Random}. Java implementations must use all the algorithms
+ * shown here for the class {@code Random}, for the sake of absolute
+ * portability of Java code. However, subclasses of class {@code Random}
* are permitted to use other algorithms, so long as they adhere to the
* general contracts for all the methods.
*
- * The algorithms implemented by class Random use a - * protected utility method that on each invocation can supply + * The algorithms implemented by class {@code Random} use a + * {@code protected} utility method that on each invocation can supply * up to 32 pseudorandomly generated bits. *
- * Many applications will find the random
method in
- * class Math
simpler to use.
+ * Many applications will find the method {@link Math#random} simpler to use.
+ *
+ *
Instances of {@code java.util.Random} are threadsafe. + * However, the concurrent use of the same {@code java.util.Random} + * instance across threads may encounter contention and consequent + * poor performance. Consider instead using + * {@link java.util.concurrent.ThreadLocalRandom} in multithreaded + * designs. + * + *
Instances of {@code java.util.Random} are not cryptographically
+ * secure. Consider instead using {@link java.security.SecureRandom} to
+ * get a cryptographically secure pseudo-random number generator for use
+ * by security-sensitive applications.
*
* @author Frank Yellin
- * @version %I%, %G%
- * @see java.lang.Math#random()
- * @since JDK1.0
+ * @since 1.0
*/
public
class Random implements java.io.Serializable {
@@ -46,10 +74,8 @@ class Random implements java.io.Serializ
* The internal state associated with this pseudorandom number generator.
* (The specs for the methods in this class describe the ongoing
* computation of this value.)
- *
- * @serial
*/
- private AtomicLong seed;
+ private final AtomicLong seed;
private final static long multiplier = 0x5DEECE66DL;
private final static long addend = 0xBL;
@@ -64,15 +90,17 @@ class Random implements java.io.Serializ
private static volatile long seedUniquifier = 8682522807148012L;
/**
- * Creates a new random number generator using a single
- * long
seed:
- *
- * Used by method next to hold - * the state of the pseudorandom number generator. + * Creates a new random number generator using a single {@code long} seed. + * The seed is the initial value of the internal state of the pseudorandom + * number generator which is maintained by method {@link #next}. + * + *- * public Random(long seed) { setSeed(seed); }
The invocation {@code new Random(seed)} is equivalent to: + *
{@code + * Random rnd = new Random(); + * rnd.setSeed(seed);}* - * @param seed the initial seed. - * @see java.util.Random#setSeed(long) + * @param seed the initial seed + * @see #setSeed(long) */ public Random(long seed) { this.seed = new AtomicLong(0L); @@ -81,134 +109,142 @@ class Random implements java.io.Serializ /** * Sets the seed of this random number generator using a single - *
long
seed. The general contract of
- * setSeed is that it alters the state of this random
- * number generator object so as to be in exactly the same state
- * as if it had just been created with the argument seed
- * as a seed. The method setSeed is implemented by class
- * Random using a thread-safe update of the seed to (seed *
- * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)
and clearing the
- * haveNextNextGaussian
flag used by {@link
- * #nextGaussian}. The implementation of setSeed by class
- * Random happens to use only 48 bits of the given
- * seed. In general, however, an overriding method may use all 64
- * bits of the long argument as a seed value.
+ * {@code long} seed. The general contract of {@code setSeed} is
+ * that it alters the state of this random number generator object
+ * so as to be in exactly the same state as if it had just been
+ * created with the argument {@code seed} as a seed. The method
+ * {@code setSeed} is implemented by class {@code Random} by
+ * atomically updating the seed to
+ * {@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}+ * and clearing the {@code haveNextNextGaussian} flag used by {@link + * #nextGaussian}. + * + *
The implementation of {@code setSeed} by class {@code Random} + * happens to use only 48 bits of the given seed. In general, however, + * an overriding method may use all 64 bits of the {@code long} + * argument as a seed value. * - * @param seed the initial seed. + * @param seed the initial seed */ synchronized public void setSeed(long seed) { seed = (seed ^ multiplier) & mask; this.seed.set(seed); - haveNextNextGaussian = false; + haveNextNextGaussian = false; } /** - * Generates the next pseudorandom number. Subclass should - * override this, as this is used by all other methods.
The
- * general contract of next is that it returns an
- * int value and if the argument bits is between
- * 1 and 32 (inclusive), then that many
- * low-order bits of the returned value will be (approximately)
- * independently chosen bit values, each of which is
- * (approximately) equally likely to be 0 or
- * 1. The method next is implemented by class
- * Random using a thread-safe update of the seed to
- * (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)
and
- * returning (int)(seed >>> (48 - bits))
. This is a
- * linear congruential pseudorandom number generator, as defined
- * by D. H. Lehmer and described by Donald E. Knuth in The Art
- * of Computer Programming, Volume 2: Seminumerical
- * Algorithms, section 3.2.1.
- *
- * @param bits random bits
- * @return the next pseudorandom value from this random number generator's sequence.
- * @since JDK1.1
+ * Generates the next pseudorandom number. Subclasses should
+ * override this, as this is used by all other methods.
+ *
+ *
The general contract of {@code next} is that it returns an + * {@code int} value and if the argument {@code bits} is between + * {@code 1} and {@code 32} (inclusive), then that many low-order + * bits of the returned value will be (approximately) independently + * chosen bit values, each of which is (approximately) equally + * likely to be {@code 0} or {@code 1}. The method {@code next} is + * implemented by class {@code Random} by atomically updating the seed to + *
{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}+ * and returning + *
{@code (int)(seed >>> (48 - bits))}.+ * + * This is a linear congruential pseudorandom number generator, as + * defined by D. H. Lehmer and described by Donald E. Knuth in + * The Art of Computer Programming, Volume 3: + * Seminumerical Algorithms, section 3.2.1. + * + * @param bits random bits + * @return the next pseudorandom value from this random number + * generator's sequence + * @since 1.1 */ protected int next(int bits) { long oldseed, nextseed; AtomicLong seed = this.seed; do { - oldseed = seed.get(); - nextseed = (oldseed * multiplier + addend) & mask; + oldseed = seed.get(); + nextseed = (oldseed * multiplier + addend) & mask; } while (!seed.compareAndSet(oldseed, nextseed)); return (int)(nextseed >>> (48 - bits)); } - private static final int BITS_PER_BYTE = 8; - private static final int BYTES_PER_INT = 4; - /** * Generates random bytes and places them into a user-supplied * byte array. The number of random bytes produced is equal to * the length of the byte array. * - * @param bytes the non-null byte array in which to put the - * random bytes. - * @since JDK1.1 + *
The method {@code nextBytes} is implemented by class {@code Random} + * as if by: + *
{@code + * public void nextBytes(byte[] bytes) { + * for (int i = 0; i < bytes.length; ) + * for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4); + * n-- > 0; rnd >>= 8) + * bytes[i++] = (byte)rnd; + * }}+ * + * @param bytes the byte array to fill with random bytes + * @throws NullPointerException if the byte array is null + * @since 1.1 */ public void nextBytes(byte[] bytes) { - int numRequested = bytes.length; - - int numGot = 0, rnd = 0; - - while (true) { - for (int i = 0; i < BYTES_PER_INT; i++) { - if (numGot == numRequested) - return; - - rnd = (i==0 ? next(BITS_PER_BYTE * BYTES_PER_INT) - : rnd >> BITS_PER_BYTE); - bytes[numGot++] = (byte)rnd; - } - } + for (int i = 0, len = bytes.length; i < len; ) + for (int rnd = nextInt(), + n = Math.min(len - i, Integer.SIZE/Byte.SIZE); + n-- > 0; rnd >>= Byte.SIZE) + bytes[i++] = (byte)rnd; } /** - * Returns the next pseudorandom, uniformly distributed
int
+ * Returns the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence. The general
- * contract of nextInt is that one int value is
+ * contract of {@code nextInt} is that one {@code int} value is
* pseudorandomly generated and returned. All 232
- * possible int values are produced with
- * (approximately) equal probability. The method nextInt is
- * implemented by class Random as follows:
- * + * possible {@code int} values are produced with + * (approximately) equal probability. + * + *- * public int nextInt() { return next(32); }
The method {@code nextInt} is implemented by class {@code Random} + * as if by: + *
{@code + * public int nextInt() { + * return next(32); + * }}* - * @return the next pseudorandom, uniformly distributed
int
- * value from this random number generator's sequence.
+ * @return the next pseudorandom, uniformly distributed {@code int}
+ * value from this random number generator's sequence
*/
- public int nextInt() { return next(32); }
+ public int nextInt() {
+ return next(32);
+ }
/**
- * Returns a pseudorandom, uniformly distributed int value
+ * Returns a pseudorandom, uniformly distributed {@code int} value
* between 0 (inclusive) and the specified value (exclusive), drawn from
* this random number generator's sequence. The general contract of
- * nextInt is that one int value in the specified range
- * is pseudorandomly generated and returned. All n possible
- * int values are produced with (approximately) equal
- * probability. The method nextInt(int n) is implemented by
- * class Random as follows:
- * - *+ * {@code nextInt} is that one {@code int} value in the specified range + * is pseudorandomly generated and returned. All {@code n} possible + * {@code int} values are produced with (approximately) equal + * probability. The method {@code nextInt(int n)} is implemented by + * class {@code Random} as if by: + *{@code * public int nextInt(int n) { - * if (n<=0) - * throw new IllegalArgumentException("n must be positive"); + * if (n <= 0) + * throw new IllegalArgumentException("n must be positive"); * - * if ((n & -n) == n) // i.e., n is a power of 2 - * return (int)((n * (long)next(31)) >> 31); + * if ((n & -n) == n) // i.e., n is a power of 2 + * return (int)((n * (long)next(31)) >> 31); * - * int bits, val; - * do { - * bits = next(31); - * val = bits % n; - * } while(bits - val + (n-1) < 0); - * return val; - * } - *
- * The hedge "approximately" is used in the foregoing description only + * int bits, val; + * do { + * bits = next(31); + * val = bits % n; + * } while (bits - val + (n-1) < 0); + * return val; + * }} + * + *
The hedge "approximately" is used in the foregoing description only * because the next method is only approximately an unbiased source of * independently chosen bits. If it were a perfect source of randomly - * chosen bits, then the algorithm shown would choose int + * chosen bits, then the algorithm shown would choose {@code int} * values from the stated range with perfect uniformity. *
* The algorithm is slightly tricky. It rejects values that would result
@@ -228,15 +264,16 @@ class Random implements java.io.Serializ
* successive calls to this method if n is a small power of two.
*
* @param n the bound on the random number to be returned. Must be
- * positive.
- * @return a pseudorandom, uniformly distributed int
- * value between 0 (inclusive) and n (exclusive).
- * @exception IllegalArgumentException n is not positive.
+ * positive.
+ * @return the next pseudorandom, uniformly distributed {@code int}
+ * value between {@code 0} (inclusive) and {@code n} (exclusive)
+ * from this random number generator's sequence
+ * @exception IllegalArgumentException if n is not positive
* @since 1.2
*/
public int nextInt(int n) {
- if (n<=0)
+ if (n <= 0)
throw new IllegalArgumentException("n must be positive");
if ((n & -n) == n) // i.e., n is a power of 2
@@ -246,25 +283,28 @@ class Random implements java.io.Serializ
do {
bits = next(31);
val = bits % n;
- } while(bits - val + (n-1) < 0);
+ } while (bits - val + (n-1) < 0);
return val;
}
/**
- * Returns the next pseudorandom, uniformly distributed long
+ * Returns the next pseudorandom, uniformly distributed {@code long}
* value from this random number generator's sequence. The general
- * contract of nextLong is that one long value is pseudorandomly
- * generated and returned. All 264
- * possible long values are produced with (approximately) equal
- * probability. The method nextLong is implemented by class
- * Random as follows:
- *
+ * return ((long)next(32) << 32) + next(32); + * }} * - * @return the next pseudorandom, uniformly distributed+ * contract of {@code nextLong} is that one {@code long} value is + * pseudorandomly generated and returned. + * + *The method {@code nextLong} is implemented by class {@code Random} + * as if by: + *
{@code * public long nextLong() { - * return ((long)next(32) << 32) + next(32); - * }
long
- * value from this random number generator's sequence.
+ * Because class {@code Random} uses a seed with only 48 bits,
+ * this algorithm will not return all possible {@code long} values.
+ *
+ * @return the next pseudorandom, uniformly distributed {@code long}
+ * value from this random number generator's sequence
*/
public long nextLong() {
// it's okay that the bottom word remains signed.
@@ -273,104 +313,113 @@ class Random implements java.io.Serializ
/**
* Returns the next pseudorandom, uniformly distributed
- * boolean
value from this random number generator's
- * sequence. The general contract of nextBoolean is that one
- * boolean value is pseudorandomly generated and returned. The
- * values true
and false
are produced with
- * (approximately) equal probability. The method nextBoolean is
- * implemented by class Random as follows:
- * - * @return the next pseudorandom, uniformly distributed - *- * public boolean nextBoolean() {return next(1) != 0;} - *
boolean
value from this random number generator's
- * sequence.
+ * {@code boolean} value from this random number generator's
+ * sequence. The general contract of {@code nextBoolean} is that one
+ * {@code boolean} value is pseudorandomly generated and returned. The
+ * values {@code true} and {@code false} are produced with
+ * (approximately) equal probability.
+ *
+ * The method {@code nextBoolean} is implemented by class {@code Random} + * as if by: + *
{@code + * public boolean nextBoolean() { + * return next(1) != 0; + * }}+ * + * @return the next pseudorandom, uniformly distributed + * {@code boolean} value from this random number generator's + * sequence * @since 1.2 */ - public boolean nextBoolean() {return next(1) != 0;} + public boolean nextBoolean() { + return next(1) != 0; + } /** - * Returns the next pseudorandom, uniformly distributed
float
- * value between 0.0
and 1.0
from this random
- * number generator's sequence. - * The general contract of nextFloat is that one float - * value, chosen (approximately) uniformly from the range 0.0f - * (inclusive) to 1.0f (exclusive), is pseudorandomly - * generated and returned. All 224 - * possible float values of the form - * m x 2-24, where - * m is a positive integer less than 224 - * , are produced with (approximately) equal probability. The - * method nextFloat is implemented by class Random as - * follows: - *
- * The hedge "approximately" is used in the foregoing description only + * return next(24) / ((float)(1 << 24)); + * }} + * + *+ * Returns the next pseudorandom, uniformly distributed {@code float} + * value between {@code 0.0} and {@code 1.0} from this random + * number generator's sequence. + * + *The general contract of {@code nextFloat} is that one + * {@code float} value, chosen (approximately) uniformly from the + * range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is + * pseudorandomly generated and returned. All 224 possible {@code float} values + * of the form m x 2-24, where m is a positive + * integer less than 224 , are + * produced with (approximately) equal probability. + * + *
The method {@code nextFloat} is implemented by class {@code Random} + * as if by: + *
{@code * public float nextFloat() { - * return next(24) / ((float)(1 << 24)); - * }
The hedge "approximately" is used in the foregoing description only * because the next method is only approximately an unbiased source of - * independently chosen bits. If it were a perfect source or randomly - * chosen bits, then the algorithm shown would choose float + * independently chosen bits. If it were a perfect source of randomly + * chosen bits, then the algorithm shown would choose {@code float} * values from the stated range with perfect uniformity.
* [In early versions of Java, the result was incorrectly calculated as: - *
+ *- * return next(30) / ((float)(1 << 30));
{@code + * return next(30) / ((float)(1 << 30));}* This might seem to be equivalent, if not better, but in fact it * introduced a slight nonuniformity because of the bias in the rounding * of floating-point numbers: it was slightly more likely that the * low-order bit of the significand would be 0 than that it would be 1.] * - * @return the next pseudorandom, uniformly distributed
float
- * value between 0.0
and 1.0
from this
- * random number generator's sequence.
+ * @return the next pseudorandom, uniformly distributed {@code float}
+ * value between {@code 0.0} and {@code 1.0} from this
+ * random number generator's sequence
*/
public float nextFloat() {
- int i = next(24);
- return i / ((float)(1 << 24));
+ return next(24) / ((float)(1 << 24));
}
/**
* Returns the next pseudorandom, uniformly distributed
- * double
value between 0.0
and
- * 1.0
from this random number generator's sequence. - * The general contract of nextDouble is that one - * double value, chosen (approximately) uniformly from the - * range 0.0d (inclusive) to 1.0d (exclusive), is - * pseudorandomly generated and returned. All - * 253 possible float - * values of the form m x 2-53 - * , where m is a positive integer less than - * 253, are produced with - * (approximately) equal probability. The method nextDouble is - * implemented by class Random as follows: - *
+ * {@code double} value between {@code 0.0} and + * {@code 1.0} from this random number generator's sequence. + * + *The general contract of {@code nextDouble} is that one + * {@code double} value, chosen (approximately) uniformly from the + * range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is + * pseudorandomly generated and returned. + * + *
The method {@code nextDouble} is implemented by class {@code Random} + * as if by: + *
{@code * public double nextDouble() { - * return (((long)next(26) << 27) + next(27)) - * / (double)(1L << 53); - * }
- * The hedge "approximately" is used in the foregoing description only - * because the next method is only approximately an unbiased - * source of independently chosen bits. If it were a perfect source or + * return (((long)next(26) << 27) + next(27)) + * / (double)(1L << 53); + * }} + * + *
The hedge "approximately" is used in the foregoing description only + * because the {@code next} method is only approximately an unbiased + * source of independently chosen bits. If it were a perfect source of * randomly chosen bits, then the algorithm shown would choose - * double values from the stated range with perfect uniformity. + * {@code double} values from the stated range with perfect uniformity. *
[In early versions of Java, the result was incorrectly calculated as: - *
+ *- * return (((long)next(27) << 27) + next(27)) - * / (double)(1L << 54);
{@code + * return (((long)next(27) << 27) + next(27)) + * / (double)(1L << 54);}* This might seem to be equivalent, if not better, but in fact it * introduced a large nonuniformity because of the bias in the rounding * of floating-point numbers: it was three times as likely that the - * low-order bit of the significand would be 0 than that it would be - * 1! This nonuniformity probably doesn't matter much in practice, but - * we strive for perfection.] - * - * @return the next pseudorandom, uniformly distributed - *
double
value between 0.0
and
- * 1.0
from this random number generator's sequence.
+ * low-order bit of the significand would be 0 than that it would be 1!
+ * This nonuniformity probably doesn't matter much in practice, but we
+ * strive for perfection.]
+ *
+ * @return the next pseudorandom, uniformly distributed {@code double}
+ * value between {@code 0.0} and {@code 1.0} from this
+ * random number generator's sequence
+ * @see Math#random
*/
public double nextDouble() {
- long l = ((long)(next(26)) << 27) + next(27);
- return l / (double)(1L << 53);
+ return (((long)(next(26)) << 27) + next(27))
+ / (double)(1L << 53);
}
private double nextNextGaussian;
@@ -378,70 +427,74 @@ class Random implements java.io.Serializ
/**
* Returns the next pseudorandom, Gaussian ("normally") distributed
- * double
value with mean 0.0
and standard
- * deviation 1.0
from this random number generator's sequence.
+ * {@code double} value with mean {@code 0.0} and standard
+ * deviation {@code 1.0} from this random number generator's sequence.
* - * The general contract of nextGaussian is that one - * double value, chosen from (approximately) the usual - * normal distribution with mean 0.0 and standard deviation - * 1.0, is pseudorandomly generated and returned. The method - * nextGaussian is implemented by class Random as if - * by a threadsafe version of the following: - *
+ * if (haveNextNextGaussian) { + * haveNextNextGaussian = false; + * return nextNextGaussian; + * } else { + * double v1, v2, s; + * do { + * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 + * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 + * s = v1 * v1 + v2 * v2; + * } while (s >= 1 || s == 0); + * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); + * nextNextGaussian = v2 * multiplier; + * haveNextNextGaussian = true; + * return v1 * multiplier; + * } + * }} * This uses the polar method of G. E. P. Box, M. E. Muller, and * G. Marsaglia, as described by Donald E. Knuth in The Art of - * Computer Programming, Volume 2: Seminumerical Algorithms, + * Computer Programming, Volume 3: Seminumerical Algorithms, * section 3.4.1, subsection C, algorithm P. Note that it generates two - * independent values at the cost of only one call to StrictMath.log - * and one call to StrictMath.sqrt. + * independent values at the cost of only one call to {@code StrictMath.log} + * and one call to {@code StrictMath.sqrt}. * - * @return the next pseudorandom, Gaussian ("normally") distributed - *+ * The general contract of {@code nextGaussian} is that one + * {@code double} value, chosen from (approximately) the usual + * normal distribution with mean {@code 0.0} and standard deviation + * {@code 1.0}, is pseudorandomly generated and returned. + * + *The method {@code nextGaussian} is implemented by class + * {@code Random} as if by a threadsafe version of the following: + *
{@code + * private double nextNextGaussian; + * private boolean haveNextNextGaussian = false; + * * public double nextGaussian() { - * if (haveNextNextGaussian) { - * haveNextNextGaussian = false; - * return nextNextGaussian; - * } else { - * double v1, v2, s; - * do { - * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 - * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 - * s = v1 * v1 + v2 * v2; - * } while (s >= 1 || s == 0); - * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); - * nextNextGaussian = v2 * multiplier; - * haveNextNextGaussian = true; - * return v1 * multiplier; - * } - * }
double
value with mean 0.0
and
- * standard deviation 1.0
from this random number
- * generator's sequence.
+ * @return the next pseudorandom, Gaussian ("normally") distributed
+ * {@code double} value with mean {@code 0.0} and
+ * standard deviation {@code 1.0} from this random number
+ * generator's sequence
*/
synchronized public double nextGaussian() {
// See Knuth, ACP, Section 3.4.1 Algorithm C.
if (haveNextNextGaussian) {
- haveNextNextGaussian = false;
- return nextNextGaussian;
- } else {
+ haveNextNextGaussian = false;
+ return nextNextGaussian;
+ } else {
double v1, v2, s;
- do {
+ do {
v1 = 2 * nextDouble() - 1; // between -1 and 1
- v2 = 2 * nextDouble() - 1; // between -1 and 1
+ v2 = 2 * nextDouble() - 1; // between -1 and 1
s = v1 * v1 + v2 * v2;
- } while (s >= 1 || s == 0);
- double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
- nextNextGaussian = v2 * multiplier;
- haveNextNextGaussian = true;
- return v1 * multiplier;
+ } while (s >= 1 || s == 0);
+ double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
+ nextNextGaussian = v2 * multiplier;
+ haveNextNextGaussian = true;
+ return v1 * multiplier;
}
}
/**
* Serializable fields for Random.
*
- * @serialField seed long;
+ * @serialField seed long
* seed for random computations
- * @serialField nextNextGaussian double;
+ * @serialField nextNextGaussian double
* next Gaussian to be returned
* @serialField haveNextNextGaussian boolean
* nextNextGaussian is valid
@@ -450,45 +503,56 @@ class Random implements java.io.Serializ
new ObjectStreamField("seed", Long.TYPE),
new ObjectStreamField("nextNextGaussian", Double.TYPE),
new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
- };
+ };
/**
- * Reconstitute the Random instance from a stream (that is,
- * deserialize it). The seed is read in as long for
- * historical reasons, but it is converted to an AtomicLong.
+ * Reconstitute the {@code Random} instance from a stream (that is,
+ * deserialize it).
*/
private void readObject(java.io.ObjectInputStream s)
throws java.io.IOException, ClassNotFoundException {
ObjectInputStream.GetField fields = s.readFields();
- long seedVal;
- seedVal = (long) fields.get("seed", -1L);
+ // The seed is read in as {@code long} for
+ // historical reasons, but it is converted to an AtomicLong.
+ long seedVal = fields.get("seed", -1L);
if (seedVal < 0)
throw new java.io.StreamCorruptedException(
"Random: invalid seed");
- seed = new AtomicLong(seedVal);
+ resetSeed(seedVal);
nextNextGaussian = fields.get("nextNextGaussian", 0.0);
haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
}
-
/**
- * Save the Random instance to a stream.
- * The seed of a Random is serialized as a long for
- * historical reasons.
- *
+ * Save the {@code Random} instance to a stream.
*/
- synchronized private void writeObject(ObjectOutputStream s) throws IOException {
+ synchronized private void writeObject(ObjectOutputStream s)
+ throws IOException {
+
// set the values of the Serializable fields
ObjectOutputStream.PutField fields = s.putFields();
+
+ // The seed is serialized as a long for historical reasons.
fields.put("seed", seed.get());
fields.put("nextNextGaussian", nextNextGaussian);
fields.put("haveNextNextGaussian", haveNextNextGaussian);
// save them
s.writeFields();
-
}
+ // Support for resetting seed while deserializing
+ private static final Unsafe unsafe = Unsafe.getUnsafe();
+ private static final long seedOffset;
+ static {
+ try {
+ seedOffset = unsafe.objectFieldOffset
+ (Random.class.getDeclaredField("seed"));
+ } catch (Exception ex) { throw new Error(ex); }
+ }
+ private void resetSeed(long seedVal) {
+ unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
+ }
}